ArticleBritish journal of cancer2018
A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features.
Article in British journal of cancer, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 128 papers, 4 of them syntheses that pooled it.
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Who cites it
128 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- The translational chasm in machine learning for triple-negative breast cancer: a quantitative landscape assessment.Frontiers in oncology · 2026Pooled it
- Pooled it
- Investigating the effects of artificial intelligence on the personalization of breast cancer management: a systematic study.BMC cancer · 2024Pooled it
- Integrated imaging and molecular analysis to decipher tumor microenvironment in the era of immunotherapy.Seminars in cancer biology · 2022Pooled it
- Deep learning for identifying radiogenomic associations in breast cancer.Computers in biology and medicine · 2019Trial
- Incremental Value of Synthetic MRI Radiomics in Predicting Neoadjuvant Response in Human Epidermal Growth Factor Receptor 2-Positive Breast Cancer.Radiology. Imaging cancer · 2026Article
- Why Only 3D for 3D? 2D-Guided Supervision for 3D Medical Vision-Language Models.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2026Article
- Application of Machine Learning for Mean Glandular Dose Prediction Utilizing DICOM Mammography Images.Journal of imaging · 2026Article
- Integrating MRI radiomics and deep learning for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer: a multicenter study.BMC medical imaging · 2026Article
- Large-scale multi-sequence pretraining for generalizable MRI analysis in versatile clinical applications.Nature biomedical engineering · 2026Article
- GuidedMorph: Two-Stage Deformable Registration for Breast MRI.IEEE journal of biomedical and health informatics · 2026Article
- MRI-based quantification of intratumoral heterogeneity for predicting recurrence risk in ER+/HER2- breast cancer.Insights into imaging · 2026Article
- Prompt mechanisms in medical imaging: A comprehensive survey.Innovation (Cambridge (Mass.)) · 2026Review
- Artificial intelligence in cancer diagnosis and therapy using sex and gender as precision biomarkers.Discover oncology · 2026Review
- Fréchet radiomic distance (FRD): A versatile metric for comparing medical imaging datasets.Medical image analysis · 2026Article
- The impact of scanner domain shift on deep learning performance in medical imaging: an experimental study.International journal of computer assisted radiology and surgery · 2026Article
- Text-Guided Multi-stage Cross-perception Network for Medical Image Segmentation.Journal of imaging informatics in medicine · 2026Article
- Radiomic signatures associated with longitudinal TNM downstaging for prognostic stratification in breast cancer.Insights into imaging · 2026Article
- AI-powered radiogenomics: imaging-driven diagnosis and discovery of cancer's molecular and cellular landscape.Molecular genetics and genomics : MGG · 2026Review
- Anatomy-guided visual prompt tuning for cross-modal breast cancer understanding.NPJ digital medicine · 2026Article
68 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
Funding
Abstract
backgroundRecent studies showed preliminary data on associations of MRI-based imaging phenotypes of breast tumours with breast cancer molecular, genomic, and related characteristics. In this study, we present a comprehensive analysis of this relationship.
methodsWe analysed a set of 922 patients with invasive breast cancer and pre-operative MRI. The MRIs were analysed by a computer algorithm to extract 529 features of the tumour and the surrounding tissue. Machine-learning-based models based on the imaging features were trained using a portion of the data (461 patients) to predict the following molecular, genomic, and proliferation characteristics: tumour surrogate molecular subtype, oestrogen receptor, progesterone receptor and human epidermal growth factor status, as well as a tumour proliferation marker (Ki-67). Trained models were evaluated on the set of the remaining 461 patients.
resultsMultivariate models were predictive of Luminal A subtype with AUC = 0.697 (95% CI: 0.647-0.746, p < .0001), triple negative breast cancer with AUC = 0.654 (95% CI: 0.589-0.727, p < .0001), ER status with AUC = 0.649 (95% CI: 0.591-0.705, p < .001), and PR status with AUC = 0.622 (95% CI: 0.569-0.674, p < .0001). Associations between individual features and subtypes we also found.
conclusionsThere is a moderate association between tumour molecular biomarkers and algorithmically assessed imaging features.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.